Majumdar Angshul, Ward Rabab K
Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
IEEE Trans Syst Man Cybern B Cybern. 2010 Oct;40(5):1359-71. doi: 10.1109/TSMCB.2009.2038493. Epub 2010 Jan 26.
The computational cost for most classification algorithms is dependent on the dimensionality of the input samples. As the dimensionality could be high in many cases, particularly those associated with image classification, reducing the dimensionality of the data becomes a necessity. The traditional dimensionality reduction methods are data dependent, which poses certain practical problems. Random projection (RP) is an alternative dimensionality reduction method that is data independent and bypasses these problems. The nearest neighbor classifier has been used with the RP method in classification problems. To obtain higher recognition accuracy, this study looks at the robustness of RP dimensionality reduction for several recently proposed classifiers--sparse classifier (SC), group SC (along with their fast versions), and the nearest subspace classifier. Theoretical proofs are offered regarding the robustness of these classifiers to RP. The theoretical results are confirmed by experimental evaluations.
大多数分类算法的计算成本取决于输入样本的维度。由于在许多情况下维度可能很高，特别是与图像分类相关的情况，因此降低数据维度成为必要。传统的降维方法依赖于数据，这带来了一些实际问题。随机投影（RP）是一种与数据无关的替代降维方法，可以避开这些问题。最近在分类问题中，最近邻分类器已与RP方法一起使用。为了获得更高的识别准确率，本研究考察了RP降维对几种最近提出的分类器（稀疏分类器（SC）、组SC（及其快速版本）和最近子空间分类器）的鲁棒性。提供了关于这些分类器对RP鲁棒性的理论证明。理论结果通过实验评估得到了证实。
